The ability of a mobile device, such as a cellular telephone, within a wireless network to estimate its speed is an important part of the device's ability to process signals received from a base station properly. In processing these signals, the mobile device demodulates pilot symbols corresponding to the base station.
The demodulated pilot symbols are the sum of estimates of the fading propagation channel (channel estimates) and background noise in the form of additive white Gaussian noise (AWGN). In conventional mobile devices, the demodulated pilot symbols are averaged by a MA-FIR filter in a channel estimation unit to reduce AWGN power. The number of demodulated pilot symbols that are averaged by the MA-FIR filter to determine one output channel estimate is referred to as the averaging length.
The averaging reduces AWGN power but introduces averaging errors. Thus, the channel estimates are degraded by AWGN, as well as by averaging errors due to fading. As the averaging length increases, the effect of AWGN decreases and the averaging errors increase. As the averaging length increases, the degradation of channel estimates initially decreases before reaching a minimum value and then increasing. Thus, the value of the averaging length for which degradation is a minimum is the optimal value for obtaining the best channel estimates.
Reducing AWGN is dependent on the averaging length; however, averaging errors are dependent on both the averaging length and the speed of the mobile device. As a result, the optimum value of the averaging length is dependent on the speed of the mobile device. Therefore, the speed of the mobile device is estimated in order to select the optimal value of the averaging length and obtain accurate channel estimates.
Conventionally, by calculating a finite number of values of the power spectral density of the demodulated pilot symbols, a mobile device may estimate an upper corner frequency and a lower corner frequency for the power spectral density of the demodulated pilot symbols and may estimate a speed for a mobile device. However, this would require an extremely large amount of processing power and would involve extremely complex computations. Thus, this method of estimating the speed of the mobile device is not feasible.
Alternatively, a smaller number of values of the power spectral density may be calculated in order to reduce computational complexity. However, as the number decreases to the point at which the computational complexity is acceptable, the error in the speed estimation may increase to an unacceptable amount.